Philippine license plate character recognition using faster R-CNN with inceptionV2
College
Gokongwei College of Engineering
Department/Unit
Electronics And Communications Engg
Document Type
Conference Proceeding
Source Title
2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019
Publication Date
11-1-2019
Abstract
This research proposes a method for automatic license plate recognition (ALPR) using the Faster R-CNN with InceptionV2 feature extractor that works in the Philippines. While there exist character recognition systems, there still remains difficulty in recognition due to different variations of Philippine license plates. By training a deep neural network in the extraction of the features in images of the different types of Philippine license plates - 1981, 2003, 2014, and others - our proposed multi-class detection system can recognize the alphanumeric characters in the license plate images. The system was tested on actual traffic images in the Philippines that contains different types of license plates, and achieved the detection rate of 90.011%, recognition rate of 93.21% and an overall accuracy of 83.895%. © 2019 IEEE.
html
Digitial Object Identifier (DOI)
10.1109/HNICEM48295.2019.9072753
Recommended Citation
Amon, M. E., Brillantes, A. M., Billones, C. D., Billones, R. C., Jose, J. C., Sybingco, E., Dadios, E. P., Fillone, A., Gan Lim, L., & Bandala, A. A. (2019). Philippine license plate character recognition using faster R-CNN with inceptionV2. 2019 IEEE 11th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2019 https://doi.org/10.1109/HNICEM48295.2019.9072753
Disciplines
Electrical and Computer Engineering
Keywords
Optical character recognition devices; Automobile license plates; Neural networks (Computer science
Upload File
wf_yes
